{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import vectorbt as vbt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "matplotlib.rcParams['figure.figsize'] = (12, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load OHLC data from cryptocurrency exchange Poloniex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done. 0.82s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O</th>\n",
       "      <th>H</th>\n",
       "      <th>L</th>\n",
       "      <th>C</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-06</th>\n",
       "      <td>242.100000</td>\n",
       "      <td>265.614570</td>\n",
       "      <td>235.75000</td>\n",
       "      <td>262.000010</td>\n",
       "      <td>1.808857e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-07</th>\n",
       "      <td>262.600000</td>\n",
       "      <td>263.643340</td>\n",
       "      <td>240.00000</td>\n",
       "      <td>250.500000</td>\n",
       "      <td>1.056409e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-08</th>\n",
       "      <td>250.500040</td>\n",
       "      <td>258.989198</td>\n",
       "      <td>243.00000</td>\n",
       "      <td>257.425741</td>\n",
       "      <td>7.780876e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-09</th>\n",
       "      <td>258.399300</td>\n",
       "      <td>280.120000</td>\n",
       "      <td>257.47453</td>\n",
       "      <td>278.849731</td>\n",
       "      <td>9.720076e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-10</th>\n",
       "      <td>278.849731</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>278.60000</td>\n",
       "      <td>325.999930</td>\n",
       "      <td>2.098731e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     O           H          L           C             V\n",
       "date                                                                   \n",
       "2017-06-06  242.100000  265.614570  235.75000  262.000010  1.808857e+07\n",
       "2017-06-07  262.600000  263.643340  240.00000  250.500000  1.056409e+07\n",
       "2017-06-08  250.500040  258.989198  243.00000  257.425741  7.780876e+06\n",
       "2017-06-09  258.399300  280.120000  257.47453  278.849731  9.720076e+06\n",
       "2017-06-10  278.849731  343.000000  278.60000  325.999930  2.098731e+07"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# period = 300, 900, 1800, 7200, 14400, or 86400\n",
    "ohlc_df = vbt.data.load_cryptopair('USDT_ETH', vbt.data.ago_dt(days=180), vbt.data.now_dt(), period=vbt.data.Period.D1)\n",
    "ohlc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# No future data\n",
    "rate_sr = ohlc_df.O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fees and slippage\n",
    "investment = 1000\n",
    "fees = 0.0025\n",
    "slippage_factor = 0.25\n",
    "slippage = (ohlc_df['H'] - ohlc_df['L']) * slippage_factor / rate_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   count        mean        std        min         25%         50%  \\\n",
      "O  180.0  298.270954  58.135747  157.38042  266.434795  297.697811   \n",
      "\n",
      "          75%    max  \n",
      "O  325.250035  473.0  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAEyCAYAAAAIiE2qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERyaGQLCIiIiKSQyFZRERERCTH/wPHghZ0O2pklwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f82b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vbt.graphics.plot_line(rate_sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate EMA indicators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "fast_ma_sr = vbt.indicators.EMA(rate_sr, span=5)\n",
    "slow_ma_sr = vbt.indicators.EMA(rate_sr, span=21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# signals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate signals based on conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. Go long whenever fast EMA is over slow EMA, while go short when opposite occurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ma_entries = vbt.signals.DMAC_entries(fast_ma_sr, slow_ma_sr)\n",
    "ma_exits = vbt.signals.DMAC_exits(fast_ma_sr, slow_ma_sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both, entry and exit vectors, are sequences of 0 and 1 (bit-vectors) to allow fast vector operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reduce both vectors to contain only signals that are first in their sequences, since we are looking for crossover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ma_entries = vbt.bitvector.first(ma_entries)\n",
    "ma_exits = vbt.bitvector.first(ma_exits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    2. Go short whenever price drops by 10%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# trailstop_exits = vbt.signals.trailstop_exits(rate_sr, ma_entries, 0.1 * rate_sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine MA exit strategy with trailing stop and pick every first exit out of sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ma_exits = vbt.bitvector.OR(ma_exits, trailstop_exits)\n",
    "# ma_exits = vbt.bitvector.first(ma_exits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To apply an additional filter, generate your own bit-vector and use `vector.AND/OR/XOR` operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generete positions out of both vectors (merge and reduce)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2017-06-07    1\n",
       "2017-06-24   -1\n",
       "2017-08-04    1\n",
       "2017-09-09   -1\n",
       "2017-09-28    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_sr = vbt.positions.from_signals(rate_sr, ma_entries, ma_exits)\n",
    "pos_sr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Position series is a binary series, where 1 = going long and -1 = going short. There is no two long/short positions in a row, since we want a clean way to evaluate a strategy (only the logic of a strategy must have an impact on the equity, not the investment size or its distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize entries and exits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   count       mean        std       min        25%       50%        75%  \\\n",
      "0    4.0  67.700912  61.406119 -9.957731  43.549708  70.89276  95.043965   \n",
      "\n",
      "          max  \n",
      "0  138.975861  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAEyCAYAAAAIiE2qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIv8PB6pa83IWj/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1209eba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vbt.positions.plot(rate_sr, pos_sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# equity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate equity from positions, based on fees and slippage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2017-06-06            NaN\n",
       "2017-06-07     975.047380\n",
       "2017-06-08     930.119603\n",
       "2017-06-09     959.449964\n",
       "2017-06-10    1035.383472\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equity_sr = vbt.equity.from_positions(rate_sr, pos_sr, investment, fees, slippage)\n",
    "equity_sr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equity is NaN before the first entry or if no positions were taken."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize base and quote equities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base: equity - hold\n",
      "   count        mean         std         min         25%         50%  \\\n",
      "0  179.0  238.900327  156.449914 -140.300118  169.359577  265.565846   \n",
      "\n",
      "          75%         max  \n",
      "0  340.339167  642.232936  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAEyCAYAAADTM+eIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1219f4908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quote: equity - hold\n",
      "   count      mean       std       min       25%       50%     75%       max\n",
      "0  179.0  0.869052  0.645523 -0.433044  0.431677  0.875992  1.0953  3.372051\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAEyCAYAAAD5gxYnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120495ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vbt.equity.plot(rate_sr, equity_sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2017-06-06    0.000000\n",
       "2017-06-07    0.000000\n",
       "2017-06-08   -0.046078\n",
       "2017-06-09    0.031534\n",
       "2017-06-10    0.079143\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns_sr = vbt.returns.from_equity(equity_sr)\n",
    "returns_sr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   count     mean       std       min       25%  50%       75%       max\n",
      "0   26.0  0.02463  0.102483 -0.136614 -0.011529  0.0  0.074471  0.328091\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAEyCAYAAADqVFbTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngxJnEREREREf/gz2xJiBKAc0VgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ff0ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vbt.returns.plot(vbt.returns.resample(returns_sr, 'W'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print summary of multiple KPIs applied on returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distribution         count         180.000000\n",
       "                     mean            0.004860\n",
       "                     std             0.040431\n",
       "                     min            -0.154448\n",
       "                     25%            -0.001580\n",
       "                     50%             0.000000\n",
       "                     75%             0.010372\n",
       "                     max             0.169088\n",
       "performance          profit          1.073858\n",
       "                     avggain         0.043035\n",
       "                     avgloss         0.031747\n",
       "                     winrate         0.539216\n",
       "                     expectancy      0.010528\n",
       "                     maxdd           0.334688\n",
       "risk/return profile  sharpe          0.120207\n",
       "                     sortino         0.140326\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vbt.performance.summary(returns_sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# optimizer.gridsearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classic optimization method is grid search (or exhaustive search). It exhaustively generates candidates from a grid of parameter values and applies them on the series. \n",
    "\n",
    "This method brings some advantages:\n",
    "- its simple to implement\n",
    "- 2d-combinations can be visualized using heatmaps\n",
    "- can be used to discover hidden patterns in combinations\n",
    "- highly parallelizable\n",
    "\n",
    "But also drawbacks are possible:\n",
    "- not flexible enough to fit changing financial markets\n",
    "- prone to overfitting\n",
    "- no intermediate feedback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grid search consists of 3-4 levels:**\n",
    "\n",
    "| Level | Motivation | Module | Structure |\n",
    "|-|-|-|-|\n",
    "| 1 | Calculate position/equity/returns maps | `srmap` | `{param: pd.Series}` |\n",
    "| 2 | Calculate KPIs | `nummap` | `pd.Series` |\n",
    "| 3 (optional) | Combine multiple KPIs into a single score and compare | `scoremap` | `pd.Series` |\n",
    "| 4 | Build heatmap to examine hidden patterns | `matrix` | `pd.DataFrame` |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end we will be able to compare performance of different trading strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L1\n",
    "## srmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import vectorbt.optimizer.gridsearch as grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate returns for a set of MA window combinations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. Precalculate all MAs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cores: 4\n",
      "processes: 1\n",
      "starmap: False\n",
      "calcs: 100 (~0.07s) ..\n",
      "done. 0.04s\n"
     ]
    }
   ],
   "source": [
    "# Init\n",
    "ma_func = lambda span: vbt.indicators.EMA(rate_sr, span=span)\n",
    "min_ma, max_ma, step = 1, 100, 1\n",
    "fees = 0.0025\n",
    "\n",
    "# Cache moving averages\n",
    "param_range = grids.params.range_params(min_ma, max_ma, step)\n",
    "ma_cache = dict(grids.srmap.from_func(ma_func, param_range))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    2. For each MA combination, generate position series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Params\n",
    "ma_space = grids.params.combine_rep_params(min_ma, max_ma, step, 2)\n",
    "\n",
    "# Func\n",
    "def ma_positions_func(fast_ma, slow_ma):\n",
    "    # Cache\n",
    "    fast_ma_sr = ma_cache[fast_ma]\n",
    "    slow_ma_sr = ma_cache[slow_ma]\n",
    "    # Signals\n",
    "    entries = vbt.signals.DMAC_entries(fast_ma_sr, slow_ma_sr)\n",
    "    entries = vbt.bitvector.first(entries)\n",
    "    exits = vbt.signals.DMAC_exits(fast_ma_sr, slow_ma_sr)\n",
    "    exits = vbt.bitvector.first(exits)\n",
    "    # Positions\n",
    "    pos_sr = vbt.positions.from_signals(rate_sr, entries, exits)\n",
    "    return pos_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cores: 4\n",
      "processes: 1\n",
      "starmap: True\n",
      "calcs: 5050 (~5.10s) ..\n",
      "done. 1.92s\n"
     ]
    }
   ],
   "source": [
    "ma_positions_srmap = grids.srmap.from_func(ma_positions_func, ma_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    3. For each position series, generate returns series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to separate position and returns calculation, since we need number of positions of each MA combination for a random map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ma_returns_func(fast_ma, slow_ma):\n",
    "    # Equity\n",
    "    pos_sr = ma_positions_srmap[(fast_ma, slow_ma)]\n",
    "    equity_sr = vbt.equity.from_positions(rate_sr, pos_sr, investment, fees, slippage)\n",
    "    # Returns\n",
    "    returns_sr = vbt.returns.from_equity(equity_sr)\n",
    "    return returns_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cores: 4\n",
      "processes: 1\n",
      "starmap: True\n",
      "calcs: 5050 (~8.90s) ..\n",
      "done. 5.23s\n"
     ]
    }
   ],
   "source": [
    "ma_returns_srmap = grids.srmap.from_func(ma_returns_func, ma_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each MA window combination generate random positions of same length and resulting returns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Params\n",
    "random_space = [(fma, sma, len(np.flatnonzero(ma_positions_srmap[(fma, sma)].values))) for fma, sma in ma_space]\n",
    "\n",
    "# Func\n",
    "def random_returns_func(slow_ma, fast_ma, n):\n",
    "    # Positions\n",
    "    pos_sr = vbt.positions.random(rate_sr, n)\n",
    "    # Equity\n",
    "    equity_sr = vbt.equity.from_positions(rate_sr, pos_sr, investment, fees, slippage)\n",
    "    # Returns\n",
    "    returns_sr = vbt.returns.from_equity(equity_sr)\n",
    "    return returns_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cores: 4\n",
      "processes: 1\n",
      "starmap: True\n",
      "calcs: 5050 (~23.16s) ..\n",
      "done. 6.68s\n"
     ]
    }
   ],
   "source": [
    "random_returns_srmap = grids.srmap.from_func(random_returns_func, random_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L2\n",
    "## nummap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply KPI on each returns series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cores: 4\n",
      "processes: 1\n",
      "starmap: False\n",
      "calcs: 5050 (~6.51s) ..\n",
      "done. 3.98s\n",
      "min (1, 5): -0.76862132663\n",
      "max (5, 21): 1.07385782616\n"
     ]
    }
   ],
   "source": [
    "if_i_hold = vbt.performance.profit(rate_sr.pct_change())\n",
    "profit = lambda r: vbt.performance.profit(r) - if_i_hold\n",
    "ma_profit_nummap = grids.nummap.from_srmap(ma_returns_srmap, vbt.performance.profit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cores: 4\n",
      "processes: 1\n",
      "starmap: False\n",
      "calcs: 5050 (~1.90s) ..\n",
      "done. 0.57s\n",
      "min (1, 5): -2.0916136364\n",
      "max (6, 19): 1.90822399689\n"
     ]
    }
   ],
   "source": [
    "sharpe = lambda r: vbt.performance.sharpe(r, nperiods=252)\n",
    "ma_sharpe_nummap = grids.nummap.from_srmap(ma_returns_srmap, sharpe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cores: 4\n",
      "processes: 1\n",
      "starmap: False\n",
      "calcs: 5050 (~4.77s) ..\n",
      "done. 3.09s\n",
      "min (1, 2, 67): -0.903138568246\n",
      "max (35, 99, 3): 2.61200514552\n"
     ]
    }
   ],
   "source": [
    "random_profit_nummap = grids.nummap.from_srmap(random_returns_srmap, vbt.performance.profit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare quantile distributions of EMA and random strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            count      mean       std       min       25%       50%       75%  \\\n",
      "nummap     5050.0  0.280062  0.268921 -0.768621  0.031299  0.330508  0.484668   \n",
      "benchmark  5050.0  0.314920  0.481939 -0.903139 -0.025826  0.265183  0.591943   \n",
      "\n",
      "                max  \n",
      "nummap     1.073858  \n",
      "benchmark  2.612005  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAEyCAYAAADqVFbTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGgjOAAAAwDT8/3X5zquGEmA7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fdedef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grids.nummap.compare_quantiles(ma_profit_nummap, random_profit_nummap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare their KPI distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            count      mean       std       min       25%       50%       75%  \\\n",
      "nummap     5050.0  0.280062  0.268921 -0.768621  0.031299  0.330508  0.484668   \n",
      "benchmark  5050.0  0.314920  0.481939 -0.903139 -0.025826  0.265183  0.591943   \n",
      "\n",
      "                max  \n",
      "nummap     1.073858  \n",
      "benchmark  2.612005  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAEyCAYAAADTM+eIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1200a5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAEyCAYAAADTM+eIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12068d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.rainbow_r\n",
    "norm = plt.Normalize()\n",
    "grids.nummap.compare_hists(ma_profit_nummap, random_profit_nummap, 50, cmap, norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L3\n",
    "## scoremap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take into account multiple weighted KPIs and generate a score from 1 to 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done. 0.01s\n",
      "min (1, 5): 1.0\n",
      "max (6, 18): 100.0\n"
     ]
    }
   ],
   "source": [
    "ma_scoremap = grids.scoremap.from_nummaps([ma_profit_nummap, ma_sharpe_nummap], [2/3, 1/3], [False, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L4\n",
    "## matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape 2d-parameter grid into a matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done. 1.81s\n"
     ]
    }
   ],
   "source": [
    "ma_matrix = grids.matrix.from_nummap(ma_profit_nummap, symmetric=True).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display as a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     count      mean       std       min       25%       50%       75%  \\\n",
      "0  10000.0  0.282862  0.268779 -0.768621  0.038997  0.330508  0.484668   \n",
      "\n",
      "        max  \n",
      "0  1.073858  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApkAAAIMCAYAAAC+BLeiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1207f0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.rainbow_r\n",
    "norm = plt.Normalize()\n",
    "matplotlib.rcParams['figure.figsize'] = (12, 9)\n",
    "grids.matrix.plot(ma_matrix, cmap, norm)\n",
    "matplotlib.rcParams['figure.figsize'] = (12, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
